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February 6, 2025

Reimagining Your SOC: Unlocking a Proactive State of Security

Reimagining your SOC Part 3/3: This blog explores the challenges security professionals face in managing cyber risk, evaluates current market solutions, and outlines strategies for building a proactive security posture.
Inside the SOC
Darktrace cyber analysts are world-class experts in threat intelligence, threat hunting and incident response, and provide 24/7 SOC support to thousands of Darktrace customers around the globe. Inside the SOC is exclusively authored by these experts, providing analysis of cyber incidents and threat trends, based on real-world experience in the field.
Written by
Gabriel Few-Wiegratz
Product Marketing Manager, Exposure Management and Incident Readiness
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06
Feb 2025

Part 1: How to Achieve Proactive Network Security

Part 2: Overcoming Alert Fatigue with AI-Led Investigations  

While the success of a SOC team is often measured through incident management effectiveness (E.g MTTD, MTTR), a true measure of maturity is the reduction of annual security incidents.

Organizations face an increasing number of alerts each year, yet the best SOC teams place focus on proactive operations which don’t reduce the threshold for what becomes an incident but targets the source risks that prevent them entirely.

Freeing up time to focus on cyber risk management is a challenge in and of itself, we cover this in the previous two blogs in this series (see above). However, when the time comes to manage risk, there are several challenges that are unique when compared to detection & response functions within cybersecurity.

Why do cyber risks matter?

While the volume of reported CVEs is increasing at an alarming rate[1], determining the criticality of each vulnerability is becoming increasingly challenging, especially when the likelihood and impact may be different for each organization. Yet vulnerabilities have stood as an important signpost in traditional security and mitigation strategies. Now, without clear prioritization, potentially severe risks may go unreported, leaving organizations exposed to significant threats.

Vulnerabilities also represent just one area of potential risks. Cyberattacks are no longer confined to a single technology type. They now traverse various platforms, including cloud services, email systems, and networks. As technology infrastructure continues to expand, so does the attack surface, making comprehensive visibility across all technology types essential for reducing risk and preventing multi-vector attacks.

However, achieving this visibility is increasingly difficult as infrastructure grows and the cyber risk market remains oversaturated. This visibility challenge extends beyond technology to include personnel and individual cyber hygiene which can still exacerbate broader cyberattacks whether malicious or not.

Organizations must adopt a holistic approach to preventative security. This includes improving visibility across all technology types, addressing human risks, and mobilizing swiftly against emerging security gaps.

“By 2026, 60% of cybersecurity functions will implement business-impact-focused risk assessment methods, aligning cybersecurity strategies with organizational objectives.” [2]

The costs of a fragmented approach

siloed preventative security measures or technologies
Figure 1: Organizations may have a combination of siloed preventative security measures or technologies in place

Unlike other security tools (like SIEM, NDR or SOAR) which contain an established set of capabilities, cyber risk reduction has not traditionally been defined by a single market, rather a variety of products and practices that each provide their own value and are overwhelming if too many are adopted. Just some examples include:

  • Threat and Vulnerability management: Leverages threat intelligence, CVEs and asset management; however, leaves teams with significant patching workflows, ignores business & human factors and is reliant on the speed of teams to keep up with each passing update.  
  • Continuous Controls Monitoring (CCM): Automatically audits the effectiveness of security controls based on industry frameworks but requires careful prioritization and human calculations to set-up effectively. Focuses solely on mobilization.
  • Breach and Attack Simulation (BAS): Automates security posture testing through mock scenarios but require previous prioritization and might not tell you how your specific technologies can be mitigated to reduce that risk.
  • Posture Management technologies: Siloed approaches across Cloud, SaaS, Data Security and even Gen AI that reactively assess misconfigurations and suggest improvements but with only industry frameworks to validate the importance of the risks.
  • Red teaming & Penetration testing: Required by several regulations including (GDPR, HIPPA, PCI, DSS), many organizations hire 'red teams' to perform real breaches in trusted conditions. Penetration tests reveal many flaws, but are not continuous, requiring third-party input and producing long to-do lists with input of broader business risk dependent on the cost of the service.
  • Third-party auditors: Organizations also use third-party auditors to identify assets with vulnerabilities, grade compliance, and recommend improvements. At best, these exercises become tick-box exercises for companies to stay in compliance with the responsibility still on the client to perform further discovery and actioning.

Many of these individual solutions on the market offer simple enhancement, or an automated version of an existing human security task. Ultimately, they lack an understanding of the most critical assets at your organization and are limited in scope, only working in a specific technology area or with the data you provide.

Even when these strategies are complete, implementation of the results require resources, coordination, and buy-in from IT, cybersecurity, and compliance departments. Given the nature of modern business structures, this can be labor and time intensive as responsibilities are shared by organizational segmentation spread across IT, governance, risk and compliance (GRC), and security teams.

Prioritize your true cyber risk with a CTEM approach

Organizations with robust security programs benefit from well-defined policies, standards, key risk indicators (KRIs), and operational metrics, making it easier to measure and report cyber risk accurately.

Implementing a framework like Gartner’s CTEM (Continuous Threat Exposure Management) can help governance by defining the most relevant risks to each organization and which specific solutions meet your improvement needs.

This five-step approach—scoping, discovery, prioritization, validation, and mobilization—encourages focused management cycles, better delegation of responsibilities and a firm emphasis on validating potential risks through technological methods like attack path modeling or breach and attack simulation to add credibility.

Implementing CTEM requires expertise and structure. This begins with an exposure management solution developed uniquely alongside a core threat detection and response offering, to provide visibility of an organization’s most critical risks, whilst linking directly to their incident-based workflows.

“By 2026, organizations prioritizing their security investments, based on a continuous threat exposure management program, will realize a two-third reduction in breaches.” [3]

Achieving a proactive security posture across the whole estate

Unlike conventional tools that focus on isolated risks, Darktrace / Proactive Exposure Management breaks down traditional barriers. Teams can define risk scopes with full, prioritized visibility of the critical risks between: IT/OT networks, email, Active Directory, cloud resources, operational groups, (or even the external attack surface by integrating with Darktrace / Attack Surface Management).

Our innovative, AI-led risk discovery provides a view that mirrors actual attacker methodologies. It does this through advanced algorithms that determine risk based on business importance, rather than traditional device-type prioritization. By implementing a sophisticated damage assessment methodology, security teams don’t just prioritize via severity but instead, the inherent impact, damage, weakness and external exposure of an asset or user.

These calculations also revolutionize vulnerability management by combining industry standard CVE measurements with that organization-specific context to ensure patch management efforts are efficient, rather than an endless list.

Darktrace also integrates MITRE ATT&CK framework mappings to connect all risks through attack path modeling. This offers validation to our AI’s scoring by presenting real world incident scenarios that could occur across your technologies, and the actionable mitigations to mobilize against them.

For those human choke points, security may also deploy targeted phishing engagements. These send real but harmless email ‘attacks’ to test employee susceptibility, strengthening your ability to identify weak points in your security posture, while informing broader governance strategies.

Combining risk with live detection and response

Together, each of these capabilities let teams take the best steps towards reducing risk and the volume of incidents they face. However, getting proactive also sharpens your ability to handle live threats if they occur.  

During real incidents Darktrace users can quickly evaluate the potential impact of affected assets, create their own risk detections based on internal policies, strengthen their autonomous response along critical attack paths, or even see the possible stage of the next attack.

By continually ingesting risk information into live triage workflows, security teams will develop a proactive-first mindset, prioritizing the assets and alerts that have the most impact to the business. This lets them utilize their resource in the most efficient way, freeing up even more time for risk management, mitigation and ensuring continuity for the business.

Whether your organization is laying the foundation for a cybersecurity program or enhancing an advanced one, Darktrace’s self-learning AI adapts to your needs:

  • Foundational stage: For organizations establishing visibility and automating detection and response.
  • Integrated stage: For teams expanding coverage across domains and consolidating tools for simplicity.
  • Proactive stage: For mature security programs enhancing posture with vulnerability management and risk prioritization.

The Darktrace ActiveAI Security Platform empowers security teams to adopt a preventative defense strategy by using Cyber AI Analyst and autonomous response to fuel quicker triage, incident handling and give time back for proactive efforts designed around business impact. The platform encapsulates the critical capabilities that help organizations be proactive and stay ahead of evolving threats.

darktrace proactive exposure management solution brief reduce risk cyber risk

Download the solution brief

Maximize security visibility and reduce risk:

  • Unify risk exposure across all technologies with AI-driven scoring for CVEs, human communications, and architectures.
  • Gain cost and ROI insights on CVE risks, breach costs, patch latency, and blind spots.
  • Strengthen employee awareness with targeted phishing simulations and training.
  • Align proactive and reactive security by assessing device compromises and prevention strategies.
  • Reduce risk with tailored guidance that delivers maximum impact with minimal effort.

Take control of your security posture today. Download here!

References

[1] https://nvd.nist.gov/vuln/search, Search all, Statistics, Total matches By Year 2023 against 2024

[2] https://www.gartner.com/en/documents/5598859

[3] https://www.gartner.com/en/articles/how-to-manage-cybersecurity-threats-not-episodes

Inside the SOC
Darktrace cyber analysts are world-class experts in threat intelligence, threat hunting and incident response, and provide 24/7 SOC support to thousands of Darktrace customers around the globe. Inside the SOC is exclusively authored by these experts, providing analysis of cyber incidents and threat trends, based on real-world experience in the field.
Written by
Gabriel Few-Wiegratz
Product Marketing Manager, Exposure Management and Incident Readiness

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February 3, 2026

The State of AI Cybersecurity 2026: Unveiling insights from over 1,500 security leaders

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2025 was the year enterprise AI went mainstream. In 2026, it’s made its way into every facet of the organizational structure – transforming workflows, revolutionizing productivity, and creating new value streams. In short, it’s opened up a whole new attack surface.  

At the same time, AI has accelerated the pace of cybersecurity arms race on both sides: adversaries are innovating using the latest AI technologies at their disposal while defenders scramble to outmaneuver them and stay ahead of AI-powered threats.  

That’s why Darktrace publishes this research every year. The State of AI Cybersecurity 2026 provides an annual snapshot of how the AI threat landscape is shifting, where organizations are adopting AI to maximum advantage, and how they are securing AI in the enterprise.

What is the State of AI Cybersecurity 2026?

We surveyed over 1,500 CISOs, IT leaders, administrators, and practitioners from a range of industries and different countries to uncover their attitudes, understanding, and priorities when it comes to AI threats, agents, tools, and operations in 2026. ​

The results show a fast-changing picture, as security leaders race to navigate the challenges and opportunities at play. Since last year, there has been enormous progress towards maturity in areas like AI literacy and confidence in AI-powered defense, while issues around AI governance remain inconclusive.

Let’s look at some of the key findings for 2026.

What’s the impact of AI on the attack surface?

Security leaders are seeing the adoption of AI agents across the workforce, and are increasingly concerned about the security implications.

  • 44% are extremely or very concerned with the security implications of third-party LLMs (like Copilot or ChatGPT)
  • 92% are concerned about the use of AI agents across the workforce and their impact on security

The rapid expansion of generative AI across the enterprise is outpacing the security frameworks designed to govern it. AI systems behave in ways that traditional defenses are not designed to monitor, introducing new risks around data exposure, unauthorized actions, and opaque decision-making as employees embed generative AI and autonomous agents into everyday workflows.  

Their top concerns? Sensitive data exposure ranks top (61%), while regulatory compliance violations are a close second (56%). These risks tend to have the fastest and most material fallout – ranging from fines to reputational harm – and are more likely to materialize in environments where AI governance is still evolving.

What’s the impact of AI on the cyber threat landscape?

AI is now being used to expedite every stage of the attack kill chain – from initial intrusion to privilege escalation and data exfiltration. 

“73% say that AI-powered threats are already having a significant impact on their organization.”

With AI, attackers can launch novel attacks at scale, and this is significantly increasing the number of threats requiring attention by the security team – often to the point of overwhelm.  

Traditional security solutions relying on historical attack data were never designed to handle an environment where attacks continuously evolve, multiply, and optimize at machine speed, so it’s no surprise that 92% agree that AI-powered cyber-threats are forcing them to significantly upgrade their defenses.

How is AI reshaping cybersecurity operations?

Cybersecurity workflows are still in flux as security leaders get used to the integration of AI agents into everyday operations.  

“Generative AI is now playing a role in 77% of security stacks.” But only 35% are using unsupervised machine learning.

AI technologies are diverse, ranging from LLMs to NLP systems, GANs, and unsupervised machine learning, with each type offering specific capabilities and facing particular limitations. The lack of familiarity with the different types of AI used within the security stack may be holding some practitioners back from using these new technologies to their best advantage.  

It also creates a lack of trust between humans and AI systems: only 14% of security professionals allow AI to take independent remediation actions in the SOC with no human in the loop.

Another new trend for this year is a strong preference (85%) for relying on Managed Security Service Providers (MSSPs) for SOC services instead of in-house teams, as organizations aim to secure expert, always-on support without the cost and operational burden of running an internal operation.

What impact is AI having on cybersecurity tools?

“96% of cybersecurity professionals agree that AI can significantly improve the speed and efficiency with which they work.”

The capacity of AI for augmenting security efforts is undisputed. But as vendor AI claims become far-reaching, it falls to security leaders to clarify which AI tools offer true value and can help solve their specific security challenges.  

Security professionals are aligned on the biggest area of impact: 72% agree that AI excels at detecting anomalies thanks to its advanced pattern recognition. This enables it to identify unusual behavior that may signal a threat, even when the specific attack has never been encountered or recorded in existing datasets.  

“When purchasing new security capabilities, 93% prefer ones that are part of a broader platform over individual point products.”

Like last year, the drive towards platform consolidation remains strong. Fewer vendors can mean tighter integrations, less console switching, streamlined management, and stronger cross-domain threat insights. The challenge is finding vendors that perform well across the board.

See the full report for more statistics and insights into how security leaders are responding to the AI landscape in 2026.

Learn more about securing AI in your enterprise.

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February 3, 2026

Introducing Darktrace / SECURE AI: Complete AI Security Across Your Enterprise

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Why securing AI can’t wait

AI is entering the enterprise faster than IT and security teams can keep up, appearing in SaaS tools, embedded in core platforms, and spun up by teams eager to move faster.  

As this adoption accelerates, it introduces unpredictable behaviors and expands the attack surface in ways existing security tools can’t see or control, startup or platform, they all lack one trait. These new types of risks command the attention of security teams and boardrooms, touching everything from business integrity to regulatory exposure.

Securing AI demands a fundamentally different approach, one that understands how AI behaves, how it interacts with data and users, and how risk emerges in real time. That shift is at the core of how organizations should be thinking about securing AI across the enterprise.

What is the current state of securing AI?

In Darktrace’s latest State of AI in Cybersecurity Report research across 1,500 cybersecurity professionals shows that the percentage of organizations without an AI adoption policy grew from 55% last year to 63% this year.

More troubling, the percentage of organizations without any plan to create an AI policy nearly tripled from 3% to 8%. Without clear policies, businesses are effectively accelerating blindfolded.

When we analyzed activity across our own customer base, we saw the same patterns playing out in their environments. Last October alone, we saw a 39% month-over-month increase in anomalous data uploads to generative AI services, with the average upload being 75MB. Given the size and frequency of these uploads, it's almost certain that much of this data should never be leaving the enterprise.

Many security teams still lack visibility into how AI is being used across their business; how it’s behaving, what it’s accessing, and most importantly, whether it’s operating safely. This unsanctioned usage quietly expands, creating pockets of AI activity that fall completely outside established security controls. The result is real organizational exposure with almost no visibility, underscoring just how widespread AI use has already become given the absence of formal policies.

This challenge doesn’t stop internally. Shadow AI extends into third-party tools, vendor platforms, and partner systems, where AI features are embedded without clear oversight.

Meanwhile, attackers are now learning to exploit AI’s unique characteristics, compounding the risks organizations are already struggling to manage.

The leader in AI cybersecurity now secures AI

Darktrace brings more than a decade of behavioral AI expertise built on an enterprise‑wide platform designed to operate in the complex, ambiguous environments where today’s AI now lives.  

Other cybersecurity technologies try to predict each new attack based on historical attacks. The problem is AI operates like humans do. Every action introduces new information that changes how AI behaves, its unpredictable, and historical attack tactics are now only a small part of the equation, forcing vendors to retrofit unproven acquisitions to secure AI.  

Darktrace is fundamentally different. Our Self‑Learning AI learns what “normal” looks like for your unique business: how your users, systems, applications, and now AI agents behave, how they communicate, and how data flows. This allows us to spot even the smallest shifts when something changes in meaningful ways. Long before AI agents were introduced, our technology was already interpreting nuance, detecting drift, uncovering hidden relationships, and making sense of ambiguous activity across networks, cloud, SaaS, email, OT, identities, and endpoints.

As AI introduces new behaviors, unstructured interactions, invisible pathways, and the rise of Shadow AI, these challenges have only intensified. But this is exactly the environment our platform was built for. Securing AI isn’t a new direction for Darktrace — it’s the natural evolution of the behavioral intelligence we’ve delivered to thousands of organizations worldwide.

Introducing Darktrace / SECURE AI – Complete AI security across your enterprise

We are proud to introduce Darktrace / SECURE AI, the newest product in the Darktrace ActiveAI Security Platform designed to secure AI across the whole enterprise.

This marks the next chapter in our mission to secure organizations from cyber threats and emerging risks. By combining full visibility, intelligent behavioral oversight, and real-time control, Darktrace is enabling enterprises to safely adopt, manage, and build AI within their business. This ensures that AI usage, data access, and behavior remain aligned to security baselines, compliance, and business goals.

Darktrace / SECURE AI can bring every AI interaction into a single view, helping teams understand intent, assess risk, protect sensitive data, and enforce policy across both human and AI Agent activity. Now organizations can embrace AI with confidence, with visibility to ensure it is operating safely, responsibly, and in alignment with their security and compliance needs.  

Because securing AI spans multiple areas and layers of complexity, Darktrace / SECURE AI is built around four foundational use cases that ensure your whole enterprise and every AI use affecting your business, whether owned or through third parties, is protected, they are:

  • Monitoring the prompts driving GenAI agents and assistants
  • Securing business AI agent identities in real time
  • Evaluating AI risks in development and deployment
  • Discovering and controlling Shadow AI

Monitoring the prompts driving GenAI agents and assistants

For AI systems, prompts are one of the most active and sensitive points of interaction—spanning human‑AI exchanges where users express intent and AI‑AI interactions where agents generate internal prompts to reason and coordinate. Because prompt language effectively is behavior, and because it relies on natural language rather than a fixed, finite syntax, the attack surface is open‑ended. This makes prompt‑driven risks far more complex than traditional API‑based vulnerabilities tied to CVEs.

Whether an attacker is probing for weaknesses, an employee inadvertently exposes sensitive data, or agents generate their own sub‑tasks to drive complex workflows, security teams must understand how prompt behavior shapes model behavior—and where that behavior can go wrong. Without that behavioral understanding, organizations face heightened risks of exploitation, drift, and cascading failures within their AI systems.

Darktrace / SECURE AI brings together all prompt activity across enterprise AI systems, including Microsoft Copilot and ChatGPT Enterprise, low‑code environments like Microsoft Copilot Studio, SaaS providers like Salesforce and Microsoft 365, and high‑code platforms such as AWS Bedrock and SageMaker, into a single, unified layer of visibility.  

Beyond visibility, Darktrace applies behavioral analytics to understand whether a prompt is unusual or risky in the context of the user, their peers, and the broader organization. Because AI attacks are far more complex and conversational than traditional exploits against fixed APIs – sharing more in common with email and Teams/Slack interactions, —this behavioral understanding is essential. By treating prompts as behavioral signals, Darktrace can detect conversational attacks, malicious chaining, and subtle prompt‑injection attempts, and where integrations allow, intervene in real time to block unsafe prompts or prevent harmful model actions as they occur.

Securing business AI agent identities in real time

As organizations adopt more AI‑driven workflows, we’re seeing a rapid rise in autonomous and semi‑autonomous agents operating across the business. These agents operate within existing identities, with the capability to access systems, read and write data, and trigger actions across cloud platforms, internal infrastructure, applications, APIs, and third‑party services. Some identities are controlled, like users, others like the ones mentioned, can appear anywhere, with organizations having limited visibility into how they’re configured or how their permissions evolve over time.  

Darktrace / SECURE AI gives organizations a real‑time, identity‑centric understanding of what their AI agents are doing, not just what they were designed to do. It automatically discovers live agent identities operating across SaaS, cloud, network, endpoints, OT, and email, including those running inside third‑party environments.  

The platform maps how each agent is configured, what systems it accesses, and how it communicates, including activity such as MCP usage or interactions with storage services where sensitive data may reside.  

By continuously observing agent behavior across all domains, Darktrace / SECURE AI highlights when unnecessary or risky permissions are granted, when activity patterns deviate, or when agents begin chaining together actions in unintended ways. This real‑time audit trail allows organizations to evaluate whether agent actions align with intended operational parameters and catch anomalous or risky behavior early.    

Evaluating AI risks in development and deployment

In the build phase, new identities are created, entitlements accumulate, components are stitched together across SaaS, cloud, and internal environments, and logic starts taking shape through prompts and configurations.  

It’s a highly dynamic and often fragmented process, and even small missteps here, such as a misconfiguration in a created agent identity, can become major security issues once the system is deployed. This is why evaluating AI risk during development and deployment is critical.

Darktrace / SECURE AI brings clarity and control across this entire lifecycle — from the moment an AI system starts taking shape to the moment it goes live. It allows you to gain visibility into created identities and their access across hyperscalers, low‑code SaaS, and internal labs, supported by AI security posture management that surfaces misconfigurations, over‑entitlement, and anomalous building events. Darktrace/ SECURE AI then connects these development insights directly to prompt oversight, connecting how AI is being built to how it will behave once deployed.  The result is a safer, more predictable AI lifecycle where risks are discovered early, guardrails are applied consistently, and innovations move forward with confidence rather than guesswork.

Discovering and controlling Shadow AI

Shadow AI has now appeared across every corner of the enterprise. It’s not just an employee pasting internal data into an external chatbot; it includes unsanctioned agent builders, hidden MCP servers, rogue model deployments, and AI‑driven workflows running on devices or services no one expected to be using AI.  

Darktrace / SECURE AI brings this frontier into view by continuously analyzing interactions across cloud, networks, endpoints, OT, and SASE environments. It surfaces unapproved AI usage wherever it appears and distinguishes legitimate activity in sanctioned tools from misuse or high‑risk behavior. The system identifies hidden AI components and rogue agents, reveals unauthorized deployments and unexpected connections to external AI systems, and highlights risky data flows that deviate from business norms.

When the behavior warrants a response, Darktrace / SECURE AI enables policy enforcement that guides users back toward sanctioned options while containing unsafe or ungoverned adoption. This closes one of the fastest‑expanding security gaps in modern enterprises and significantly reduces the attack surface created by shadow AI.

Conclusion

What’s needed now along with policies and frameworks for AI adoption is the right tooling to detect threats based on AI behavior across shadow use, prompt risks, identity misuse, and AI development.  

Darktrace is uniquely positioned to secure AI, we’ve spent over a decade building AI that learns your business – understanding subtle behavior across the entire enterprise long before AI agents arrived. With over 10,000 customers relying on Darktrace as the last line of defense to capture threats others cannot, Securing AI isn’t a pivot for us, it's not an acquisition; it’s the natural extension of the behavioral expertise and enterprise‑wide intelligence our platform was built on from the start.  

To learn more about how to secure AI at your organization we curated a readiness program that brings together IT and security leaders navigating this responsibility, providing a forum to prepare for high-impact decisions, explore guardrails, and guide the business amid growing uncertainty and pressure.

Sign up for the Secure AI Readiness Program here: This gives you exclusive access to the latest news on the latest AI threats, updates on emerging approaches shaping AI security, and insights into the latest innovations, including Darktrace’s ongoing work in this area.

Ready to talk with a Darktrace expert on securing AI? Register here to receive practical guidance on the AI risks that matter most to your business, paired with clarity on where to focus first across governance, visibility, risk reduction, and long-term readiness.  

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About the author
Brittany Woodsmall
Product Marketing Manager, AI
Your data. Our AI.
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